This relates generally to shared virtual memory implementations.
The computing industry is moving towards a heterogeneous platform architecture consisting of a general purpose CPU along with programmable GPUs attached both as a discrete or integrated device. These GPUs are connected over both coherent and non-coherent interconnects, have different industry standard architectures (ISAs) and may use their own operating systems.
Computing platforms composed of a combination of a general purpose processor (CPU) and a graphics processor (GPU) have become ubiquitous, especially in the client computing space. Today, almost all desktop and notebook platforms ship with one or more CPUs along with an integrated or a discrete GPU. For example, some platforms have a processor paired with an integrated graphics chipset, while the remaining use a discrete graphics processor connected over an interface, such as PCI-Express. Some platforms ship as a combination of a CPU and a GPU. For example, some of these include a more integrated CPU-GPU platform while others include a graphics processor to complement integrated GPU offerings.
These CPU-GPU platforms may provide significant performance boost on non-graphics workloads in image processing, medical imaging, data mining, and other domains. The massively data parallel GPU may be used for getting high throughput on the highly parallel portions of the code. Heterogeneous CPU-GPU platforms may have a number of unique architectural constraints such as:                The GPU may be connected in both integrated and discrete forms. For example, some graphics processors are integrated with the chipset. On the other hand other current GPUs are attached in a discrete manner over an interface such as PCI-Express. While the hardware may provide cache coherence between a CPU and integrated graphics processor, it may be difficult to do that for a discrete GPU. A system may also have a hybrid configuration where a low-power lower-performance GPU is integrated with a CPU, with a higher-performance discrete GPU. Finally, a platform may also have multiple GPU cards.        The CPU and GPU may have different operating systems. For example, a processor may have its own operating system kernel. This means that the virtual memory translation schemes may be different between the CPU and GPU. The same virtual address may be simultaneously mapped to two different physical addresses through two different page tables on the CPU and GPU. This also means that the system environment (loaders, linkers, etc.) may be different between the CPU and GPU. For example, the loader may load the application at different base addresses on the CPU and GPU.        The CPU and the GPU may have different ISAs and hence the same code may not be run on both the processors.        